To solve this optimization problem, we first need to define the decision variables, the objective function, and the constraints.

Let's denote:
- $G$ as the number of pairs of grey pants produced.
- $B$ as the number of pairs of black pants produced.

The profit per pair of grey pants is not directly given, but for simplicity, let's assume the selling price is higher than the cost by a fixed amount. However, since the problem does not specify the selling prices, we will maximize the production based on the constraints provided, assuming that producing more pairs (within the constraints) is better.

The objective function to maximize profit would typically be of the form: Maximize $P_G \cdot G + P_B \cdot B$, where $P_G$ and $P_B$ are the profits per pair of grey and black pants, respectively. However, without specific selling prices (and thus profits), we will focus on maximizing production within the given constraints as a proxy for maximizing profit.

The costs given are:
- Cost per pair of grey pants: $25
- Cost per pair of black pants: $15

However, these are not directly used in our formulation since we lack the selling prices. Instead, we'll focus on the processing time constraints.

Constraints based on processing times and availability:
1. At Wimo: $40G + 20B \leq 2100$
2. At Webo: $30G + 15B \leq 3000$

Non-negativity constraints:
- $G \geq 0$
- $B \geq 0$

Given the problem does not specify selling prices, we will adjust our approach to simply maximize production (which indirectly maximizes revenue and thus potentially profit), using a proxy objective function that could be to minimize the costs or directly maximize production quantities. However, without explicit profit margins, directly maximizing production under constraints is a reasonable alternative goal.

For simplicity in solving this problem with Gurobi, we will set up an optimization model to find the maximum number of pants (both grey and black) that can be produced within these constraints, assuming producing more is generally better for the company.

```python
from gurobipy import *

# Create a new model
m = Model("Clothing_Production")

# Decision variables: Number of pairs of grey and black pants to produce
G = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="GreyPants")
B = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="BlackPants")

# Objective function: Maximize total production (as a proxy for maximizing profit)
m.setObjective(G + B, GRB.MAXIMIZE)

# Constraints
# 1. Wimo's processing time constraint
m.addConstr(40*G + 20*B <= 2100, name="Wimo_Time")

# 2. Webo's processing time constraint
m.addConstr(30*G + 15*B <= 3000, name="Webo_Time")

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Grey Pants: {G.x}")
    print(f"Black Pants: {B.x}")
else:
    print("No optimal solution found.")
```